Computer aided detection of architectural distortion in mammography

Abstract

A method for detecting architectural distortion within mammographic image data. The method identifies breast tissue within the image data, generates an orientation field and a corresponding magnitude field within the identified breast tissue and generates a feature map by processing the orientation field with a phase portrait model at one or more image scales. The method identifies one or more architectural distortion features according to the generated feature map and displays the one or more identified architectural distortion features.

1 . A method for detecting architectural distortion within mammographic image data, comprising:
 identifying breast tissue within the image data;   generating an orientation field and a corresponding magnitude field within the identified breast tissue;   generating a feature map by processing the orientation field with a phase portrait model at one or more image scales;   identifying one or more architectural distortion features according to the generated feature map; and   displaying the one or more identified architectural distortion features.   
     
     
         2 . The method of  claim 1  wherein identifying breast tissue comprises identifying a skin line. 
     
     
         3 . The method of  claim 1  wherein generating an orientation field comprises applying one or more Gabor filters to identified breast tissue portions of the mammographic image data. 
     
     
         4 . The method of  claim 1  wherein identifying the breast tissue further comprises down-sampling the image data. 
     
     
         5 . The method of  claim 1  further comprising applying a high-pass filtering operation to at least identified breast tissue portions of the mammographic image data. 
     
     
         6 . The method of  claim 1  wherein generating a feature map comprises calculating an eigenvalue for a transformation matrix and generating feature map data according to the calculated eigenvalue. 
     
     
         7 . The method of  claim 1  wherein identifying the one or more architectural distortion features comprises obtaining either or both a maximum value and an entropy value using the generated feature map. 
     
     
         8 . The method of  claim 1  further comprising using the one or more identified architectural distortion features to train a classification system. 
     
     
         9 . The method of  claim 1  further comprising obtaining parameter values entered by a user for conditioning the image data, for conditioning orientation field generation, or for conditioning feature map generation. 
     
     
         10 . The method of  claim 9  wherein obtaining the parameter values comprises obtaining, from a configuration file that is manipulated by the user, values for using a Gabor filter bank. 
     
     
         11 . The method of  claim 1  further comprising displaying an indicator of relative risk for one or more of the identified architectural distortion features. 
     
     
         12 . The method of  claim 1  further comprising applying simulated annealing for matching between a phase portrait template and underlying image structure defined in the generated orientation field. 
     
     
         13 . The method of  claim 1  wherein generating a feature map comprises obtaining phase portrait matching results having a node pattern. 
     
     
         14 . The method of  claim 3  wherein generating an orientation field further comprises applying non-maximum suppression to the results from Gabor filtering. 
     
     
         15 . The method of  claim 1  further comprising processing the feature map using a Gaussian discrimination function to select a subset of features that discriminate architectural distortion tissue from normal tissue. 
     
     
         16 . A detection system for architectural distortion in mammography comprising:
 an input image processor that is responsive to stored instructions for obtaining a digital or digitized mammography image;   a computer-aided detection system having an architectural distortion detection processor that is responsive to stored instructions for generating a breast tissue structure orientation field as well as generating a corresponding magnitude field, for generating a node feature map by matching one or more predetermined multi-scale phase portrait templates with underlying image structure in the orientation field, for extracting architectural distortion related features from the generated feature map, for selecting a subset of features that discriminate architectural distortion tissue from normal tissue, and for building a neural network classifier based on a selected subset of features; and   a display operatively connected with the computer-aided detection system and actuable to display the extracted architectural distortion related features.   
     
     
         17 . The detection system of  claim 16  further comprising an operator interface configurable to accept viewer instructions and adjustments to parameters that condition the performance of the computer-aided detection system. 
     
     
         18 . The detection system of  claim 16  further comprising one or more configuration files for providing parameters used by the computer-aided detection system.